: Sleep loss is increasingly recognized as a significant factor in aging-related dementias with widespread impact on health and cognition. The extent of sleep loss has increased consistently for several decades, with modern society suffering from shortened sleep due to factors such as work hours, `social jetlag', parental responsibilities and technology. Sleep loss has a demonstrated interaction with aging phenotypes, including in Alzheimer's and Parkinson's diseases, but is also suspected to be a risk factor for conversion of younger populations to dementias later in life. For example, sleep loss makes the `young seem old' with respect to cognition, while quality sleep in younger populations may be neuroprotective later in life. However, little is known about the distinct mechanisms by which sleep interacts with organismal metabolism and how this disruption overlaps with aging phenotypes. Furthermore, quantitative cognitive assessments have shown that individuals are differentially susceptible to sleep loss, and these sleep loss `resistant' or `susceptible' characteristics are trait-like in that they transcend temporal experimental boundaries and are heritable. Here we propose to profile metabolite changes in blood as a function of neurobehavioral response to sleep loss by leveraging data previously collected in a controlled total sleep deprivation (TSD) study. We hypothesize that the metabolite markers of TSD will be similar to those found previously in our studies of sleep restriction as well as published aging studies, and that the metabolite concentrations and rhythms will be sensitive to individual neurobehavioral response. We will test our overall hypothesis in three independent but overlapping aims designed to probe the relationship between neurocognitive response to TSD, changes in diurnal metabolite rhythms, and novel biomarkers of sleep loss. ? Aim 1. To define the variation in known blood metabolic markers of sleep loss as function of neurobehavioral susceptibility to total sleep deprivation using a targeted metabolomics approach. Hypothesis: We expect to observe a blunted response in those metabolites indicative of sleep debt in a population of individuals who are resistant to sleep loss as defined by PVT response. ? Aim 2. To use a comprehensive and untargeted metabolomics approach to elucidate novel indicators of sleep vulnerability and total sleep deprivation. Hypothesis: An increased number of metabolites responsive to TSD compared to previous markers of SR will be detected, and that these markers will overlap with existing markers in neurocognitive deficits such as Parkinson's and Alzheimer's disease. ? Aim 3. To establish the impact of total sleep deprivation on diurnal rhythms of metabolite fluctuations, and the differential metabolite patterns with respect to neurobehavioral susceptibility. Hypothesis: Diurnal rhythms will be enhanced in amplitude in those with increased susceptibility, and that similar to observed changes in gene expression, the response will be reduced in individuals resistant to TSD.